The DOI matches its Crossref record, with a small difference in title or year; 98 of the 112 references it cites were found in a registry or an index.
Approaches to Automated Detection of Cyberbullying: A Survey
Open accessAbstract
Research into cyberbullying detection has increased in recent years, due in part to the proliferation of cyberbullying across social media and its detrimental effect on young people. A growing body of work is emerging on automated approaches to cyberbullying detection. These approaches utilise machine learning and natural language processing techniques to identify the characteristics of a cyberbullying exchange and automatically detect cyberbullying by matching textual data to the identified traits. In this paper, we present a systematic review of published research (as identified via Scopus, ACM and IEEE Xplore bibliographic databases) on cyberbullying detection approaches. On the basis of our extensive literature review, we categorise existing approaches into 4 main classes, namely supervised learning, lexicon-based, rule-based, and mixed-initiative approaches. Supervised learning-based approaches typically use classifiers such as SVM and Naıve Bayes to develop predictive models for cyberbullying detection. Lexicon-based systems utilise word lists and use the presence of words within the lists to detect cyberbullying. Rule-based approaches match text to predefined rules to identify bullying, and mixed-initiatives approaches combine human-based reasoning with one or more of the aforementioned approaches. We found lack of labelled datasets and non-holistic consideration of cyberbullying by researchers when developing detection systems are two key challenges facing cyberbullying detection research. This paper essentially maps out the state-of-the-art in cyberbullying detection research and serves as a resource for researchers to determine where to best direct their future research efforts in this field.
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“These studies do not show publicly available datasets for cyberbullying detection, and a few survey papers address the open issues and challenges [26,27].”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [26]
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“For example, 'The image that you have sent so irritated me and I would rather not contact with you any longer!' is not easy to detect as cyberbullying without investigating from a rationale factor, albeit that model shows negative sentiment [26].”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [26]
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“Language challenge: Capturing context and analyzing the sentiment from different types of sentences is a difficult task and challenging work for cyberbullying detection.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [26]
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Metadata
- Authors
- Salawu, S., He, Y., & Lumsden, J.
- Affiliations
- Aston University (GB)
- Venue
- IEEE Transactions on Affective Computing
- Published
- 2020-01-01
- Volume
- 11
- Issue
- 1
- Pages
- 3-24
- ISSN
- 1949-3045 (ISSN-L)
- 2371-9850
- OpenAlex
- W2762029282
- Cited by
- 258
- References
- 112
- Citations by year
- 2026: 22 · 2025: 27 · 2024: 33 · 2023: 41 · 2022: 29 · 2021: 44 · 2020: 26
- FWCI
- 10.11
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- IEEE Transactions on Affective Computing (publishedVersion)
- Aston Publications Explorer (Aston University) (submittedVersion, public-domain)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Psychology › Social Psychology › Bullying, Victimization, and Aggression
- Keywords
- cyberbullying detection, automated detection, social media, supervised learning, rule-based detection, annotated dataset, text classification, youth
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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